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@@ -65,6 +65,21 @@ The [CPO_SIMPO](https://github.com/fe1ixxu/CPO_SIMPO/tree/main) repository did p
## Released Models
### v0.2
We found that using a strong reward model for annotating preference optimization datasets is crucial. In this iteration, we have reannotated the dataset [princeton-nlp/llama3-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/llama3-ultrafeedback-armorm) using a more powerful reward model, [RLHFlow/ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1). As a result, the v0.2 models demonstrate significantly improved performance compared to the v0.1 models.
| models | | AE2 LC | AE2 WR | AH |
|------------------------------|-----------------------------------------------------------------------------------------------------------|:------:|:------:|:----:|
| Llama 3 Instruct 8B RRHF v0.2 | [princeton-nlp/Llama-3-Instruct-8B-RRHF-v2.0](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-RRHF-v0.2) | 37.9 | 31.6 | 28.8 |
| Llama 3 Instruct 8B SLiC-HF v0.2 | [princeton-nlp/Llama-3-Instruct-8B-SLiC-HF-v2.0](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SLiC-HF-v0.2) | 33.9 | 32.5 | 29.3 |
| Llama 3 Instruct 8B DPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-DPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-DPO-v0.2) | 48.2 | 47.5 | 35.2 |
| Llama 3 Instruct 8B IPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-IPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-IPO-v0.2) | 46.8 | 42.4 | 36.6 |
| Llama 3 Instruct 8B CPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2) | 34.1 | 36.4 | 30.9 |
| Llama 3 Instruct 8B KTO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-KTO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-KTO-v0.2) | 34.1 | 32.1 | 27.3 |
| Llama 3 Instruct 8B ORPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2) | 38.1 | 33.8 | 28.2 |
| Llama 3 Instruct 8B R-DPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-RDPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-RDPO-v0.2) | 48.0 | 45.8 | 35.1 |
| Llama 3 Instruct 8B SimPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2) | 53.7 | 47.5 | 36.5 |
### v0.1
Below is the complete list of models evaluated in our preprint. We used the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset to train the Mistral Base and Llama3 Base models, the [princeton-nlp/mistral-instruct-ultrafeedback](https://huggingface.co/datasets/princeton-nlp/mistral-instruct-ultrafeedback) dataset to train the Mistral Instruct models, and the [princeton-nlp/llama3-ultrafeedback](https://huggingface.co/datasets/princeton-nlp/llama3-ultrafeedback) dataset to train the Llama3 Instruct models. The latter two datasets are annotated by the [llm-blender/PairRM](https://huggingface.co/llm-blender/PairRM) model.
@@ -111,20 +126,6 @@ models |
| Llama3 Instruct 8B R-DPO | [princeton-nlp/Llama-3-Instruct-8B-RDPO](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-RDPO) | 41.1 | 37.8 | 33.1 |
| Llama3 Instruct 8B SimPO | [princeton-nlp/Llama-3-Instruct-8B-SimPO](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO) | 44.7 | 40.5 | 33.8 |
### v0.2
We found that using a strong reward model for annotating preference optimization datasets is crucial. In this iteration, we have reannotated the dataset [princeton-nlp/llama3-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/llama3-ultrafeedback-armorm) using a more powerful reward model, [RLHFlow/ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1). As a result, the v0.2 models demonstrate significantly improved performance compared to the v0.1 models.
| models | | AE2 LC | AE2 WR | AH |
|------------------------------|-----------------------------------------------------------------------------------------------------------|:------:|:------:|:----:|
| Llama 3 Instruct 8B RRHF v0.2 | [princeton-nlp/Llama-3-Instruct-8B-RRHF-v2.0](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-RRHF-v0.2) | 37.9 | 31.6 | 28.8 |
| Llama 3 Instruct 8B SLiC-HF v0.2 | [princeton-nlp/Llama-3-Instruct-8B-SLiC-HF-v2.0](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SLiC-HF-v0.2) | 33.9 | 32.5 | 29.3 |
| Llama 3 Instruct 8B DPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-DPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-DPO-v0.2) | 48.2 | 47.5 | 35.2 |
| Llama 3 Instruct 8B IPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-IPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-IPO-v0.2) | 46.8 | 42.4 | 36.6 |
| Llama 3 Instruct 8B CPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2) | 34.1 | 36.4 | 30.9 |
| Llama 3 Instruct 8B KTO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-KTO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-KTO-v0.2) | 34.1 | 32.1 | 27.3 |
| Llama 3 Instruct 8B ORPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2) | 38.1 | 33.8 | 28.2 |
| Llama 3 Instruct 8B R-DPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-RDPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-RDPO-v0.2) | 48.0 | 45.8 | 35.1 |
| Llama 3 Instruct 8B SimPO v0.2 | [princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2) | 53.7 | 47.5 | 36.5 |
### Use our models for inference
Please refer to the [generate.py](generate.py) script for detailed instructions on loading the model with the appropriate chat template.